• Smart Structures and Systems
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• v.6 no.1
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• pp.1-16
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• 2010

This paper presents a novel smart MEMS accelerometer which employs a hybrid control algorithm and an estimator. This scheme is realized by adding a sliding-mode controller to a conventional PID closed loop system to achieve higher stability and higher dynamic range and to prevent pull-in phenomena by preventing finger displacement from passing a maximum preset value as well as adding an adaptive nonlinear observer to a conventional PID closed loop system. This estimator is used for online estimation of the parameter variations for MEMS accelerometers and gives the capability of self testing to the system. The analysis of convergence and resolution show that while the proposed control scheme satisfies these criteria it also keeps resolution performance better than what is normally obtained in conventional PID controllers. The performance of the proposed hybrid controller investigated here is validated by computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.203-208
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• 1998

This paper presents an adaptive fuzzy controller using an neural network and adaptation algorithm. Reference-model following neuro-fuzzy controller(RMFNFC) is invesgated in order to overcome the difficulty of rule selecting and defects of the membership function in the general fuzzy logic controller(FLC). RMFNFC is developed to tune various parameter of the fuzzy controller which is used for the discrete nonlinear system control. RMFNFC is trained with the identification information and control closed loop error. A closed loop error is used for design criteria of a fuzzy controller which characterizes and quantize the control performance required in the overall control system. A control system is trained up the controller with the variation of the system obtained from the identifier and closed loop error. Numerical examples are presented to control of the discrete nonlinear system. Simulation results show the effectiveness of the proposed controller.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.5
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• pp.46-50
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• 1997

In fuzzy logic control, static fuzzy rules cannot cope with significant changes of parameters of plants or environment. To solve this prohlem, self-organizing fuzzy control. neural-network-hased fuzzy logic control and so on have heen introduced so far. However, dynamically changed fuzzy rules of these schemes may make a fuzzy logic controller Fall into dangerous situations because the changed fuzzy rules may he incomplete or inconsistent. This paper proposes a new adaptive filzzy logic control scheme using a predictivc neural network. Although some parameters of a controlled plant or environment are changed, proposed fuzzy logic controller changes its decision outputs adaptively and robustly using unchanged initial fuzzy rules and the predictive errors generated hy the predictive neural network by on-line learning. Experimental results with a D<' servo-motor position control problem show that propnsed cnntrol scheme is very useful in the viewpoint of adaptability.

• Journal of Power Electronics
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• v.17 no.1
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• pp.149-160
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• 2017

To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

• The Journal of Korean Association of Computer Education
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• v.9 no.2
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• pp.69-78
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• 2006

Educational evaluation is not the work to rank learners hierarchically, but the thing to increase the educational effectiveness by solving a learner's problem and improving the education process from the proper evaluation. The conventional evaluation systems have measured a learner's recognition level by dichotomy. Although they support the evaluation depending on a learner's academic ability and supply the feedback for wrong selection, it is insufficient to take out study-motive and give the establishment for a guidance point of learning. In this paper, we propose the web-based individual adaptive testing system in considering partial score for a learner. Our system are effective to estimate the ability of learner by considering partial score in detail and offer an feedback study from the self-diagnosis function for a learning results.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.287-297
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• 2018

To improve the performance of sensorless induction motor (IM) drives, a novel speed estimation method based on the real-time adaptive extended Kalman filter (RAEKF) is proposed in this paper. In this algorithm, the fuzzy factor is introduced to tune the measurement covariance matrix online by the degree of mismatch between the actual innovation and the theoretical. Simultaneously, the fuzzy factor can be continuously self-tuned tuned by the fuzzy logic reasoning system based on Takagi-Sugeno (T-S) model. Therefore, the proposed method improves the model adaptability to the actual systems and the environmental variations, and reduces the speed estimation error. Furthermore, a simple exponential function based on the fuzzy theory is used to reduce the computational burden, and the real-time performance of the system is improved. The correctness and the effectiveness of the proposed method are verified by the simulation and experimental results.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.9
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• pp.762-771
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• 2012

In this paper, we propose an adaptive power control scheme employing a self-optimization concept in femtocell systems, in order to improve system capacity, thereby reducing call-drop probability. In the proposed scheme, each femto base station(FBS) controls individual channel's transmission power base on two parameters; the neighboring cell's transmission power for each individual channel which is delivered from a femto-gateway and the received power strength from neighboring cells which is periodically measured by means of a spectrum sensing. Adaptive adjustment of individual channel's transmission power in accordance with femto mobile station(FMS) mobility features can also reduce undesirable handovers and evenly distribute traffic load over all femtocells. In addition, the manipulative control of channel's transmission power is able to keep the system coverage and the call-drop probability within an acceptable range, regardless of density of femtocells. Computer simulation shows that the proposed scheme outperforms existing schemes in terms of the system coverage and the call-drop probability.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.2
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• pp.11-17
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• 2018

IDS/IPS and networked computer systems are playing an increasingly important role in our society. They have been the targets of a malicious attacks that actually turn into intrusions. That is why computer security has become an important concern for network administrators. Recently, various Detection/Prevention System schemes have been proposed based on various technologies. However, the techniques, which have been applied in many systems is useful for existing intrusion patterns on standard-only systems. Therefore, probe detection of private clouds using BlockChain has become a major security protection technology to detection potential attacks. In addition, BlockChain and Probe detection need to take into account the relationship between the various factors. We should develop a new probe detection technology that uses BlockChain to fine new pattern detection probes in cloud service security in the end. In this paper, we propose a probe detection using Fuzzy Cognitive Map(FCM) and Self Adaptive Module(SAM) based on service security using BlockChain technology.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.453-459
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• 2008

The neural network is currently being used throughout numerous control system fields. However, it is not easy to obtain an input-output pattern when the neural network is used for the system of a single feedback controller and it is difficult to obtain satisfactory performance with when the load changes rapidly or disturbance is applied. To resolve these problems, this paper proposes a new mode to implement a neural network controller by installing a real object for control and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The real plant object for controlling of this mode implements a simple neural network controller replacing the activation function and provides the error back propagation path to calculate the error at the output node. As the controller is designed using a simple structure neural network, the input-output pattern problem is solved naturally and real-time learning becomes possible through the general error back propagation algorithm. The new algorithm applied neural network controller gives excellent performance for initial and tracking response and shows a robust performance for rapid load change and disturbance, in which the permissible error surpasses the range border. The effect of the proposed control algorithm was verified in a test that controlled the speed of a motor equipped with a high speed computing capable DSP on which the proposed algorithm was loaded.